With the rapid increase of mobile data traffic and the increasing popularization of intelligent terminal devices, a wireless video streaming media technology represented by mobile video service has been used more and more widely in recent years. In the meanwhile, mobile users present a more complicated heterogeneous property in the aspects of used mobile terminal devices, video-on-demand contents, network connectivity and the like, thereby greatly increasing the complexity and difficulty of video streaming media services. The dynamic adaptive streaming media technology can provide different versions of the same video content for users to improve user's satisfaction in video watching in a heterogeneous network. Herein, each video version is encoded using specified bit rate and/or resolution, such that each user can determine and download the most appreciate video version according to own video-on-demand needs and network conditions.
On the one hand, a single-bit rate video encoding technology requires consuming extremely high encoding complexity to achieve higher video compression performance. For an encoding server performing the dynamic adaptive streaming media technology, it is often limited by its own physical power consumption so that excess video encoding versions are obtained by encoding to be adapted to needs of different users, and thus it is needed to reasonably allocate limited computation resources to different versions of each video. On the other hand, due to constraints on a server storage space and constraints on a bottleneck bandwidth in network transmission, a sum of bit rates of different versions of all the videos is also limited, and thus it is also needed to reasonably allocate limited bit rate resources to different versions of each video.
Through retrieval of the existing technologies, we find that a paper entitled with “Optimal selection of adaptive streaming representations” is disclosed by Toni et al. in ACM Transactions on Multimedia Computing, Communications, and Applications in February, 2015) and a paper entitled with “Transcoding live adaptive video streams at a massive scale in the cloud” is disclosed by R. Aparicio-Pardo et al. in the Proceedings of ACM Multimedia Systems Conference in March 2015, pp. 49-60. These two papers respectively studied how the server optimally selects an encoding bit rate and resolution for each video version under the condition that the encoding bit rate and the power consumption are restricted. However, the main approach of the above work is to model an optimal version selection problem into an extremely complicated integer linear programming (ILP) problem, and an optimal video encoding version is obtained by solving this ILP problem. The complexity of this approach increases exponentially with the scale of the system, and thus it is needed to consume extremely high complexity and computation resources, thereby taking up the computation resources for video encoding by the server, with great limitation. On the other hand, the above work assumes that the different video versions of each video have been obtained by pre-encoding, its final objective is to select an optimal version subset from these known versions, and this assumption is failed in an actual system. For example, all the video streams transmit bit rates to the users after undergoing real-time encoding, and thus there is no enough time to pre-encode all the videos.